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14 Commits
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e434e69183 |
@@ -1838,7 +1838,7 @@ static common_chat_params common_chat_templates_apply_legacy(
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if (res < 0) {
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// if the custom "tmpl" is not supported, we throw an error
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// this is a bit redundant (for good), since we're not sure if user validated the custom template with llama_chat_verify_template()
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throw std::runtime_error("this custom template is not supported");
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throw std::runtime_error("this custom template is not supported, try using --jinja");
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}
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// if it turns out that our buffer is too small, we resize it
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@@ -556,11 +556,8 @@ class TextModel(ModelBase):
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logger.info(f"gguf: experts used count = {n_experts_used}")
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if (head_dim := self.hparams.get("head_dim")) is not None:
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# Workaround for incorrect AutoConfig value for DeepSeekV3 (is set correctly in DeepSeekV2Model class)
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# https://github.com/huggingface/transformers/blob/19224c3642705c5b6988c9f5f4251f83323d05ae/src/transformers/models/deepseek_v3/configuration_deepseek_v3.py#L210
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if self.hparams.get("model_type") != "deepseek_v3":
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self.gguf_writer.add_key_length(head_dim)
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self.gguf_writer.add_value_length(head_dim)
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self.gguf_writer.add_key_length(head_dim)
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self.gguf_writer.add_value_length(head_dim)
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self.gguf_writer.add_file_type(self.ftype)
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logger.info(f"gguf: file type = {self.ftype}")
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@@ -1901,9 +1898,7 @@ class LlamaModel(TextModel):
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hparams = self.hparams
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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if "head_dim" in hparams:
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rope_dim = hparams["head_dim"]
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else:
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if (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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@@ -1985,7 +1980,8 @@ class LlamaModel(TextModel):
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
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if (dim := self.hparams.get("head_dim")) is None:
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dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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factor = rope_scaling.get("factor", 8.0)
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@@ -2321,9 +2317,7 @@ class DeciModel(TextModel):
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hparams = self.hparams
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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if "head_dim" in hparams:
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rope_dim = hparams["head_dim"]
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else:
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if (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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@@ -2363,7 +2357,8 @@ class DeciModel(TextModel):
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
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if (dim := self.hparams.get("head_dim")) is None:
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dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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factor = rope_scaling.get("factor", 8.0)
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@@ -3681,9 +3676,7 @@ class InternLM3Model(TextModel):
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hparams = self.hparams
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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if "head_dim" in hparams:
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rope_dim = hparams["head_dim"]
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else:
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if (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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@@ -5098,9 +5091,7 @@ class DeepseekModel(TextModel):
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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hparams = self.hparams
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if "head_dim" in hparams:
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rope_dim = hparams["head_dim"]
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else:
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if (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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@@ -5990,7 +5981,8 @@ class ExaoneModel(TextModel):
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if rope_scaling := self.find_hparam(["rope_scaling"], optional=True):
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if rope_scaling.get("rope_type", '').lower() == "llama3":
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base = self.hparams.get("rope_theta", 10000.0)
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dim = self.hparams.get("head_dim", self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
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if (dim := self.hparams.get("head_dim")) is None:
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dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
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freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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factor = rope_scaling.get("factor", 8.0)
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@@ -6102,7 +6094,8 @@ class BailingMoeModel(TextModel):
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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hparams = self.hparams
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rope_dim = hparams.get("head_dim") or hparams["hidden_size"] // hparams["num_attention_heads"]
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if (rope_dim := hparams.get("head_dim")) is None:
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rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"]
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self.gguf_writer.add_rope_dimension_count(rope_dim)
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rope_scaling = self.hparams.get("rope_scaling") or {}
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@@ -6134,7 +6127,8 @@ class BailingMoeModel(TextModel):
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n_head = self.hparams["num_attention_heads"]
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n_kv_head = self.hparams.get("num_key_value_heads")
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n_embd = self.hparams["hidden_size"]
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head_dim = self.hparams.get("head_dim") or n_embd // n_head
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if (head_dim := self.hparams.get("head_dim")) is None:
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head_dim = n_embd // n_head
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output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT)
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@@ -368,6 +368,8 @@ if (MSVC)
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/wd4005 # Macro redefinition
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/wd4244 # Conversion from one type to another type, possible loss of data
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/wd4267 # Conversion from 'size_t' to a smaller type, possible loss of data
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/wd4305 # Conversion from 'type1' to 'type2', possible loss of data
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/wd4566 # Conversion from 'char' to 'wchar_t', possible loss of data
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/wd4996 # Disable POSIX deprecation warnings
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/wd4702 # Unreachable code warnings
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)
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@@ -387,4 +389,46 @@ if (MSVC)
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disable_msvc_warnings(ggml-cpu-skylakex)
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disable_msvc_warnings(ggml-cpu-icelake)
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disable_msvc_warnings(ggml-cpu-alderlake)
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if (GGML_BUILD_EXAMPLES)
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disable_msvc_warnings(common-ggml)
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disable_msvc_warnings(common)
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disable_msvc_warnings(mnist-common)
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disable_msvc_warnings(mnist-eval)
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disable_msvc_warnings(mnist-train)
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disable_msvc_warnings(gpt-2-ctx)
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disable_msvc_warnings(gpt-2-alloc)
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disable_msvc_warnings(gpt-2-backend)
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disable_msvc_warnings(gpt-2-sched)
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disable_msvc_warnings(gpt-2-quantize)
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disable_msvc_warnings(gpt-2-batched)
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disable_msvc_warnings(gpt-j)
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disable_msvc_warnings(gpt-j-quantize)
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disable_msvc_warnings(magika)
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disable_msvc_warnings(yolov3-tiny)
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disable_msvc_warnings(sam)
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disable_msvc_warnings(simple-ctx)
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disable_msvc_warnings(simple-backend)
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endif()
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if (GGML_BUILD_TESTS)
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disable_msvc_warnings(test-mul-mat)
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disable_msvc_warnings(test-arange)
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disable_msvc_warnings(test-backend-ops)
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disable_msvc_warnings(test-cont)
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disable_msvc_warnings(test-conv-transpose)
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disable_msvc_warnings(test-conv-transpose-1d)
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disable_msvc_warnings(test-conv1d)
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disable_msvc_warnings(test-conv2d)
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disable_msvc_warnings(test-conv2d-dw)
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disable_msvc_warnings(test-customop)
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disable_msvc_warnings(test-dup)
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disable_msvc_warnings(test-opt)
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disable_msvc_warnings(test-pool)
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endif ()
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endif()
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@@ -330,6 +330,10 @@ if (GGML_CPU_ALL_VARIANTS)
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ggml_add_cpu_backend_variant(android_armv8.2_1 DOTPROD)
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ggml_add_cpu_backend_variant(android_armv8.2_2 DOTPROD FP16_VECTOR_ARITHMETIC)
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ggml_add_cpu_backend_variant(android_armv8.6_1 DOTPROD FP16_VECTOR_ARITHMETIC MATMUL_INT8)
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elseif (APPLE)
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ggml_add_cpu_backend_variant(apple_m1 DOTPROD)
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ggml_add_cpu_backend_variant(apple_m2_m3 DOTPROD MATMUL_INT8)
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ggml_add_cpu_backend_variant(apple_m4 DOTPROD MATMUL_INT8 NOSVE SME)
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else()
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message(FATAL_ERROR "Unsupported ARM target OS: ${CMAKE_SYSTEM_NAME}")
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endif()
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@@ -190,6 +190,9 @@ function(ggml_add_cpu_backend_variant_impl tag_name)
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set(ARCH_TAGS "${ARCH_TAGS}+sve2")
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list(APPEND ARCH_DEFINITIONS GGML_USE_SVE2)
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endif()
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if (GGML_INTERNAL_NOSVE)
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set(ARCH_TAGS "${ARCH_TAGS}+nosve")
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endif()
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if (GGML_INTERNAL_SME)
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set(ARM_MCPU "armv9.2-a")
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set(ARCH_TAGS "${ARCH_TAGS}+sme")
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@@ -1,88 +0,0 @@
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#pragma once
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// Solve alias issue for Apple targets (currently PowerPC, x86, and ARM64).
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// Mach-O has a weak alias equivalent but no practical compiler support can
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// be found, so we need to do it manually.
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// ref: https://stackoverflow.com/questions/42757744
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//
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// This file is a complement to native implementations in the `arch` folder.
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// A kernel in quants.c or repack.cpp is either:
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// - implemented in the `arch` folder, or
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// - defined in this file to remove the `_generic` suffix
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#if defined(GGML_CPU_GENERIC)
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// quants.c
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#define quantize_row_q8_0_generic quantize_row_q8_0
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#define quantize_row_q8_1_generic quantize_row_q8_1
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#define quantize_row_q8_K_generic quantize_row_q8_K
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#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
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#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
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#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
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#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
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#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
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#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
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#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
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#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
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#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
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#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
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#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
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#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
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#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
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#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
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#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
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#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
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#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
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#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
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#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
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#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
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#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
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// repack.cpp
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#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
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#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
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#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
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#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
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#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
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#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
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#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
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#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
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#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
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#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
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#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
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#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
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#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
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#elif defined(__aarch64__) || defined(__arm__)
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// repack.cpp
|
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#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
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#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
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#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
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#elif defined(__x86_64__) || defined(__i386__)
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// repack.cpp
|
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#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
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#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
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#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
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#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
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#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
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#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
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#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
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#elif defined(__POWERPC__)
|
||||
// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
|
||||
// quants.c
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#endif
|
||||
184
ggml/src/ggml-cpu/arch-fallback.h
Normal file
184
ggml/src/ggml-cpu/arch-fallback.h
Normal file
@@ -0,0 +1,184 @@
|
||||
#pragma once
|
||||
|
||||
// Rename `_generic` functions if no native implementation is available.
|
||||
// This effectively selects the generic implementation.
|
||||
|
||||
#if defined(GGML_CPU_GENERIC)
|
||||
// quants.c
|
||||
#define quantize_row_q8_0_generic quantize_row_q8_0
|
||||
#define quantize_row_q8_1_generic quantize_row_q8_1
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_q4_0_q8_0_generic ggml_vec_dot_q4_0_q8_0
|
||||
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
|
||||
#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
|
||||
#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
|
||||
#define ggml_vec_dot_q8_0_q8_0_generic ggml_vec_dot_q8_0_q8_0
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
|
||||
#define ggml_vec_dot_q3_K_q8_K_generic ggml_vec_dot_q3_K_q8_K
|
||||
#define ggml_vec_dot_q4_K_q8_K_generic ggml_vec_dot_q4_K_q8_K
|
||||
#define ggml_vec_dot_q5_K_q8_K_generic ggml_vec_dot_q5_K_q8_K
|
||||
#define ggml_vec_dot_q6_K_q8_K_generic ggml_vec_dot_q6_K_q8_K
|
||||
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
|
||||
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
|
||||
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
|
||||
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
|
||||
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
|
||||
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
|
||||
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__aarch64__) || defined(__arm__) || defined(_M_ARM) || defined(_M_ARM64)
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#elif defined(__x86_64__) || defined(__i386__) || defined(_M_IX86) || defined(_M_X64)
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__POWERPC__) || defined(__powerpc__)
|
||||
// ref: https://github.com/ggml-org/llama.cpp/pull/14146#issuecomment-2972561679
|
||||
// quants.c
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__loongarch64)
|
||||
// quants.c
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__riscv)
|
||||
// quants.c
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
|
||||
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
|
||||
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
|
||||
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
|
||||
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
|
||||
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
|
||||
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__s390x__)
|
||||
// quants.c
|
||||
#define quantize_row_q8_K_generic quantize_row_q8_K
|
||||
#define ggml_vec_dot_q5_0_q8_0_generic ggml_vec_dot_q5_0_q8_0
|
||||
#define ggml_vec_dot_q5_1_q8_1_generic ggml_vec_dot_q5_1_q8_1
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_q2_K_q8_K_generic ggml_vec_dot_q2_K_q8_K
|
||||
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
|
||||
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
|
||||
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
|
||||
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
|
||||
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
|
||||
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#elif defined(__wasm__)
|
||||
// quants.c
|
||||
#define ggml_vec_dot_q4_1_q8_1_generic ggml_vec_dot_q4_1_q8_1
|
||||
#define ggml_vec_dot_tq1_0_q8_K_generic ggml_vec_dot_tq1_0_q8_K
|
||||
#define ggml_vec_dot_tq2_0_q8_K_generic ggml_vec_dot_tq2_0_q8_K
|
||||
#define ggml_vec_dot_iq2_xxs_q8_K_generic ggml_vec_dot_iq2_xxs_q8_K
|
||||
#define ggml_vec_dot_iq2_xs_q8_K_generic ggml_vec_dot_iq2_xs_q8_K
|
||||
#define ggml_vec_dot_iq2_s_q8_K_generic ggml_vec_dot_iq2_s_q8_K
|
||||
#define ggml_vec_dot_iq3_xxs_q8_K_generic ggml_vec_dot_iq3_xxs_q8_K
|
||||
#define ggml_vec_dot_iq3_s_q8_K_generic ggml_vec_dot_iq3_s_q8_K
|
||||
#define ggml_vec_dot_iq1_s_q8_K_generic ggml_vec_dot_iq1_s_q8_K
|
||||
#define ggml_vec_dot_iq1_m_q8_K_generic ggml_vec_dot_iq1_m_q8_K
|
||||
#define ggml_vec_dot_iq4_nl_q8_0_generic ggml_vec_dot_iq4_nl_q8_0
|
||||
#define ggml_vec_dot_iq4_xs_q8_K_generic ggml_vec_dot_iq4_xs_q8_K
|
||||
// repack.cpp
|
||||
#define ggml_quantize_mat_q8_0_4x4_generic ggml_quantize_mat_q8_0_4x4
|
||||
#define ggml_quantize_mat_q8_0_4x8_generic ggml_quantize_mat_q8_0_4x8
|
||||
#define ggml_quantize_mat_q8_K_4x8_generic ggml_quantize_mat_q8_K_4x8
|
||||
#define ggml_gemv_q4_0_4x4_q8_0_generic ggml_gemv_q4_0_4x4_q8_0
|
||||
#define ggml_gemv_q4_0_4x8_q8_0_generic ggml_gemv_q4_0_4x8_q8_0
|
||||
#define ggml_gemv_q4_0_8x8_q8_0_generic ggml_gemv_q4_0_8x8_q8_0
|
||||
#define ggml_gemv_q4_K_8x8_q8_K_generic ggml_gemv_q4_K_8x8_q8_K
|
||||
#define ggml_gemv_iq4_nl_4x4_q8_0_generic ggml_gemv_iq4_nl_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x4_q8_0_generic ggml_gemm_q4_0_4x4_q8_0
|
||||
#define ggml_gemm_q4_0_4x8_q8_0_generic ggml_gemm_q4_0_4x8_q8_0
|
||||
#define ggml_gemm_q4_0_8x8_q8_0_generic ggml_gemm_q4_0_8x8_q8_0
|
||||
#define ggml_gemm_q4_K_8x8_q8_K_generic ggml_gemm_q4_K_8x8_q8_K
|
||||
#define ggml_gemm_iq4_nl_4x4_q8_0_generic ggml_gemm_iq4_nl_4x4_q8_0
|
||||
#endif
|
||||
@@ -371,7 +371,7 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b)
|
||||
#define vec_xor(a, b) ((a) ^ (b)) // Vector XOR
|
||||
#endif
|
||||
|
||||
typedef signed char char8x16_t __attribute__((vector_size(16)));
|
||||
typedef signed char char8x16_t __attribute__((vector_size(16)));
|
||||
typedef unsigned char uchar8x16_t __attribute__((vector_size(16)));
|
||||
|
||||
typedef int8_t int8x16_t __attribute__((vector_size(16)));
|
||||
@@ -382,10 +382,10 @@ typedef uint8_t uint8x16_t __attribute__((vector_size(16)));
|
||||
typedef uint16_t uint16x8_t __attribute__((vector_size(16)));
|
||||
typedef uint32_t uint32x4_t __attribute__((vector_size(16)));
|
||||
|
||||
typedef float float32x4_t __attribute__((vector_size(16)));
|
||||
typedef double double64x2_t __attribute((vector_size(16)));
|
||||
typedef float float32x4_t __attribute__((vector_size(16)));
|
||||
typedef double double64x2_t __attribute__((vector_size(16)));
|
||||
|
||||
typedef signed long long long64x2_t __attribute((vector_size(16)));
|
||||
typedef signed long long long64x2_t __attribute__((vector_size(16)));
|
||||
typedef unsigned long long ulong64x2_t __attribute__((vector_size(16)));
|
||||
|
||||
typedef struct ggml_uint8x16x2_t {
|
||||
@@ -509,28 +509,3 @@ int ggml_threadpool_chunk_add(struct ggml_threadpool * tp, int value);
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
#define GGML_DO_PRAGMA_(x) _Pragma (#x)
|
||||
#define GGML_DO_PRAGMA(x) GGML_DO_PRAGMA_(x)
|
||||
#if defined(GGML_CPU_GENERIC) || defined(__HIPCC__) || defined(__APPLE__)
|
||||
// Note for Apple targets:
|
||||
// - clang: aliases are not supported on darwin
|
||||
// - all native kernels need to be implemented in both x86 and arm files
|
||||
// - on iOS, tvOS, and visionOS, if cmake cannot determine the target architecture, all `_generic` names are replaced by defines
|
||||
# define GGML_WEAK_ALIAS(name, alias)
|
||||
#elif defined(__GNUC__)
|
||||
// GCC/Clang on *nix
|
||||
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(weak name = alias) // NOLINT
|
||||
#elif defined(_MSC_VER) && defined(_WIN64)
|
||||
// MSVC
|
||||
// Note: C name mangling varies across different calling conventions
|
||||
// see https://learn.microsoft.com/en-us/cpp/build/reference/decorated-names?view=msvc-170
|
||||
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:" #name "=" #alias))
|
||||
#elif defined(_MSC_VER) && defined(WIN32)
|
||||
// ref: https://github.com/ggml-org/whisper.cpp/pull/3239#issuecomment-2958224591
|
||||
# define GGML_WEAK_ALIAS(name, alias) GGML_DO_PRAGMA(comment(linker, "/alternatename:_" #name "=_" #alias))
|
||||
#else
|
||||
# error "Unsupported compiler for GGML_WEAK_ALIAS"
|
||||
#endif
|
||||
|
||||
#define GGML_CPU_NATIVE_IMPL(name) GGML_WEAK_ALIAS(name, name ## _generic)
|
||||
|
||||
@@ -5,9 +5,7 @@
|
||||
#include "ggml-quants.h"
|
||||
#include "quants.h"
|
||||
|
||||
#if defined(__APPLE__)
|
||||
#include "apple-fallback.h"
|
||||
#endif
|
||||
#include "arch-fallback.h"
|
||||
|
||||
#include <string.h>
|
||||
#include <assert.h>
|
||||
@@ -42,12 +40,10 @@ void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
|
||||
void quantize_row_q8_0_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
|
||||
quantize_row_q8_0_ref(x, y, k);
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(quantize_row_q8_0)
|
||||
|
||||
void quantize_row_q8_1_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
|
||||
quantize_row_q8_1_ref(x, y, k);
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(quantize_row_q8_1)
|
||||
|
||||
//
|
||||
// 2-6 bit quantization in super-blocks
|
||||
@@ -108,7 +104,6 @@ void quantize_row_tq2_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy,
|
||||
void quantize_row_q8_K_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int64_t k) {
|
||||
quantize_row_q8_K_ref(x, y, k);
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(quantize_row_q8_K)
|
||||
|
||||
//===================================== Dot products =================================
|
||||
|
||||
@@ -147,7 +142,6 @@ void ggml_vec_dot_q4_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_0_q8_0)
|
||||
|
||||
// TODO: add WASM SIMD
|
||||
void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
@@ -185,7 +179,6 @@ void ggml_vec_dot_q4_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_1_q8_1)
|
||||
|
||||
void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -229,7 +222,6 @@ void ggml_vec_dot_q5_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_0_q8_0)
|
||||
|
||||
void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
const int qk = QK8_1;
|
||||
@@ -273,7 +265,6 @@ void ggml_vec_dot_q5_1_q8_1_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_1_q8_1)
|
||||
|
||||
void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -304,7 +295,6 @@ void ggml_vec_dot_q8_0_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q8_0_q8_0)
|
||||
|
||||
void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(nrc == 1);
|
||||
@@ -357,7 +347,6 @@ void ggml_vec_dot_tq1_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq1_0_q8_K)
|
||||
|
||||
void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(nrc == 1);
|
||||
@@ -390,7 +379,6 @@ void ggml_vec_dot_tq2_0_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_tq2_0_q8_K)
|
||||
|
||||
void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(nrc == 1);
|
||||
@@ -443,7 +431,6 @@ void ggml_vec_dot_q2_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
}
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q2_K_q8_K)
|
||||
|
||||
void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -523,7 +510,6 @@ void ggml_vec_dot_q3_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
for (int l = 0; l < 8; ++l) sumf += sums[l];
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q3_K_q8_K)
|
||||
|
||||
void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -599,7 +585,6 @@ void ggml_vec_dot_q4_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
for (int l = 0; l < 8; ++l) sumf += sums[l];
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q4_K_q8_K)
|
||||
|
||||
void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -680,7 +665,6 @@ void ggml_vec_dot_q5_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
for (int l = 0; l < 8; ++l) sumf += sums[l];
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q5_K_q8_K)
|
||||
|
||||
void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -736,7 +720,6 @@ void ggml_vec_dot_q6_K_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, c
|
||||
for (int l = 0; l < 8; ++l) sumf += sums[l];
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_q6_K_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -779,7 +762,6 @@ void ggml_vec_dot_iq2_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs
|
||||
}
|
||||
*s = 0.125f * sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xxs_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -830,7 +812,6 @@ void ggml_vec_dot_iq2_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
*s = 0.125f * sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_xs_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -883,7 +864,6 @@ void ggml_vec_dot_iq2_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
|
||||
*s = 0.125f * sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq2_s_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -928,7 +908,6 @@ void ggml_vec_dot_iq3_xxs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs
|
||||
}
|
||||
*s = 0.25f * sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_xxs_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -985,7 +964,6 @@ void ggml_vec_dot_iq3_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq3_s_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -1029,7 +1007,6 @@ void ggml_vec_dot_iq1_s_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_s_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(n % QK_K == 0);
|
||||
@@ -1091,7 +1068,6 @@ void ggml_vec_dot_iq1_m_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq1_m_q8_K)
|
||||
|
||||
void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(nrc == 1);
|
||||
@@ -1121,7 +1097,6 @@ void ggml_vec_dot_iq4_nl_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_nl_q8_0)
|
||||
|
||||
void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
|
||||
assert(nrc == 1);
|
||||
@@ -1168,7 +1143,6 @@ void ggml_vec_dot_iq4_xs_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
*s = sumf;
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_vec_dot_iq4_xs_q8_K)
|
||||
|
||||
// ============================ 4-bit non-linear quants
|
||||
|
||||
|
||||
@@ -8,9 +8,7 @@
|
||||
#include "ggml-cpu-impl.h"
|
||||
#include "traits.h"
|
||||
|
||||
#if defined(__APPLE__)
|
||||
#include "apple-fallback.h"
|
||||
#endif
|
||||
#include "arch-fallback.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <cstring>
|
||||
@@ -87,7 +85,6 @@ void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GG
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x4)
|
||||
|
||||
void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
|
||||
assert(QK8_0 == 32);
|
||||
@@ -126,7 +123,6 @@ void ggml_quantize_mat_q8_0_4x8_generic(const float * GGML_RESTRICT x, void * GG
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_0_4x8)
|
||||
|
||||
void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) {
|
||||
assert(QK_K == 256);
|
||||
@@ -178,7 +174,6 @@ void ggml_quantize_mat_q8_K_4x8_generic(const float * GGML_RESTRICT x, void * GG
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_quantize_mat_q8_K_4x8)
|
||||
|
||||
} // extern "C"
|
||||
|
||||
@@ -248,7 +243,6 @@ void ggml_gemv_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x4_q8_0)
|
||||
|
||||
void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -293,7 +287,6 @@ void ggml_gemv_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
for (int j = 0; j < ncols_interleaved; j++) s[x * ncols_interleaved + j] = sumf[j];
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_4x8_q8_0)
|
||||
|
||||
void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -340,7 +333,6 @@ void ggml_gemv_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_0_8x8_q8_0)
|
||||
|
||||
void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK_K;
|
||||
@@ -419,7 +411,6 @@ void ggml_gemv_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemv_q4_K_8x8_q8_K)
|
||||
|
||||
void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -466,7 +457,6 @@ void ggml_gemv_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemv_iq4_nl_4x4_q8_0)
|
||||
|
||||
void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -523,7 +513,6 @@ void ggml_gemm_q4_0_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x4_q8_0)
|
||||
|
||||
void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -578,7 +567,6 @@ void ggml_gemm_q4_0_4x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_4x8_q8_0)
|
||||
|
||||
void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -633,7 +621,6 @@ void ggml_gemm_q4_0_8x8_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_0_8x8_q8_0)
|
||||
|
||||
void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK_K;
|
||||
@@ -723,7 +710,6 @@ void ggml_gemm_q4_K_8x8_q8_K_generic(int n, float * GGML_RESTRICT s, size_t bs,
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemm_q4_K_8x8_q8_K)
|
||||
|
||||
void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc) {
|
||||
const int qk = QK8_0;
|
||||
@@ -780,7 +766,6 @@ void ggml_gemm_iq4_nl_4x4_q8_0_generic(int n, float * GGML_RESTRICT s, size_t bs
|
||||
}
|
||||
}
|
||||
}
|
||||
GGML_CPU_NATIVE_IMPL(ggml_gemm_iq4_nl_4x4_q8_0)
|
||||
|
||||
} // extern "C"
|
||||
|
||||
|
||||
@@ -64,10 +64,6 @@ static_assert(sizeof(block_iq4_nlx4) == 4 * sizeof(ggml_half) + QK4_NL * 2, "wro
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
// Workaround for clang:
|
||||
// clang++ complains: ``error: call to 'ggml_gemm_q4_0_4x4_q8_0' is ambiguous''
|
||||
// repro: https://godbolt.org/z/oKdeWKonM (ICE), https://godbolt.org/z/1szq6P36v (ambiguous call)
|
||||
#if defined(GGML_CPU_CLANG_WORKAROUND) || defined(__APPLE__) || !(defined(__GNUC__) && defined(__clang__)) || defined(__HIPCC__)
|
||||
void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
void ggml_quantize_mat_q8_0_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
void ggml_quantize_mat_q8_K_4x8(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
@@ -81,7 +77,6 @@ void ggml_gemm_q4_0_4x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const vo
|
||||
void ggml_gemm_q4_0_8x8_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_q4_K_8x8_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
void ggml_gemm_iq4_nl_4x4_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy, int nr, int nc);
|
||||
#endif // !defined(__clang__)
|
||||
|
||||
// Native implementations
|
||||
void ggml_quantize_mat_q8_0_4x4_generic(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k);
|
||||
|
||||
@@ -944,10 +944,8 @@ static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
|
||||
for (int i = 0; i < offset; ++i) { \
|
||||
x[i] = vec_add(x[i], x[offset + i]); \
|
||||
} \
|
||||
res = vec_extract(x[0], 0) + \
|
||||
vec_extract(x[0], 1) + \
|
||||
vec_extract(x[0], 2) + \
|
||||
vec_extract(x[0], 3); \
|
||||
float32x4_t tmp = x[0] + vec_reve(x[0]); \
|
||||
res = tmp[0] + tmp[1]; \
|
||||
}
|
||||
|
||||
#define GGML_F32_VEC GGML_F32x4
|
||||
|
||||
@@ -2664,7 +2664,9 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
|
||||
ggml_backend_buft_is_cuda_split(node->src[j]->buffer->buft) || (integrated && ggml_backend_buft_is_cuda_host(node->src[j]->buffer->buft)));
|
||||
}
|
||||
}
|
||||
#endif
|
||||
#else
|
||||
GGML_UNUSED(integrated);
|
||||
#endif // NDEBUG
|
||||
|
||||
bool ok = ggml_cuda_compute_forward(*cuda_ctx, node);
|
||||
if (!ok) {
|
||||
|
||||
@@ -144,9 +144,15 @@ if (Vulkan_FOUND)
|
||||
${VULKAN_SHADER_GEN_CMAKE_ARGS}
|
||||
|
||||
BUILD_COMMAND ${CMAKE_COMMAND} --build . --config $<CONFIG>
|
||||
INSTALL_COMMAND ${CMAKE_COMMAND} --install . --config $<CONFIG>
|
||||
|
||||
# NOTE: When DESTDIR is set using Makefile generators and
|
||||
# "make install" triggers the build step, vulkan-shaders-gen
|
||||
# would be installed into the DESTDIR prefix, so it is unset
|
||||
# to ensure that does not happen.
|
||||
|
||||
INSTALL_COMMAND ${CMAKE_COMMAND} -E env --unset=DESTDIR
|
||||
${CMAKE_COMMAND} --install . --config $<CONFIG>
|
||||
)
|
||||
ExternalProject_Add_StepTargets(vulkan-shaders-gen build install)
|
||||
|
||||
set (_ggml_vk_host_suffix $<IF:$<STREQUAL:${CMAKE_HOST_SYSTEM_NAME},Windows>,.exe,>)
|
||||
set (_ggml_vk_genshaders_dir "${CMAKE_BINARY_DIR}/$<CONFIG>")
|
||||
@@ -172,8 +178,6 @@ if (Vulkan_FOUND)
|
||||
|
||||
DEPENDS ${_ggml_vk_shader_files}
|
||||
vulkan-shaders-gen
|
||||
vulkan-shaders-gen-build
|
||||
vulkan-shaders-gen-install
|
||||
|
||||
COMMENT "Generate vulkan shaders"
|
||||
)
|
||||
|
||||
@@ -888,12 +888,6 @@ struct ggml_context {
|
||||
struct ggml_object * objects_end;
|
||||
};
|
||||
|
||||
struct ggml_context_container {
|
||||
bool used;
|
||||
|
||||
struct ggml_context context;
|
||||
};
|
||||
|
||||
//
|
||||
// data types
|
||||
//
|
||||
|
||||
@@ -1 +1 @@
|
||||
6a7d170c04789f6ebcf320ed03c1b16973f93bd7
|
||||
8cda0a3c19f2c7dc493887353c42f6956bc268b1
|
||||
|
||||
@@ -333,7 +333,7 @@ int32_t llm_chat_apply_template(
|
||||
std::string role(message->role);
|
||||
if (role == "system") {
|
||||
// there is no system message for gemma, but we will merge it with user prompt, so nothing is broken
|
||||
system_prompt = trim(message->content);
|
||||
system_prompt += trim(message->content);
|
||||
continue;
|
||||
}
|
||||
// in gemma, "assistant" is "model"
|
||||
@@ -355,7 +355,7 @@ int32_t llm_chat_apply_template(
|
||||
std::string role(message->role);
|
||||
if (role == "system") {
|
||||
// there is no system message support, we will merge it with user prompt
|
||||
system_prompt = message->content;
|
||||
system_prompt += message->content;
|
||||
continue;
|
||||
} else if (role == "user") {
|
||||
ss << "Human: ";
|
||||
|
||||
@@ -187,7 +187,7 @@ struct clip_hparams {
|
||||
float eps = 1e-6;
|
||||
float rope_theta = 0.0;
|
||||
|
||||
std::vector<int32_t> image_grid_pinpoints;
|
||||
std::vector<clip_image_size> image_res_candidates; // for llava-uhd style models
|
||||
int32_t image_crop_resolution;
|
||||
std::unordered_set<int32_t> vision_feature_layer;
|
||||
int32_t attn_window_size = 0;
|
||||
@@ -2109,8 +2109,7 @@ struct clip_model_loader {
|
||||
if (is_vision) {
|
||||
get_u32(KEY_IMAGE_SIZE, hparams.image_size);
|
||||
get_u32(KEY_PATCH_SIZE, hparams.patch_size);
|
||||
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
|
||||
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, hparams.image_grid_pinpoints, false);
|
||||
get_u32(KEY_IMAGE_CROP_RESOLUTION, hparams.image_crop_resolution, false);
|
||||
get_i32(KEY_MINICPMV_VERSION, hparams.minicpmv_version, false); // legacy
|
||||
|
||||
} else if (is_audio) {
|
||||
@@ -2120,6 +2119,20 @@ struct clip_model_loader {
|
||||
GGML_ASSERT(false && "unknown modality");
|
||||
}
|
||||
|
||||
// for pinpoints, we need to convert it into a list of resolution candidates
|
||||
{
|
||||
std::vector<int> pinpoints;
|
||||
get_arr_int(KEY_IMAGE_GRID_PINPOINTS, pinpoints, false);
|
||||
if (!pinpoints.empty()) {
|
||||
for (size_t i = 0; i < pinpoints.size(); i += 2) {
|
||||
hparams.image_res_candidates.push_back({
|
||||
pinpoints[i],
|
||||
pinpoints[i+1],
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// default warmup value
|
||||
hparams.warmup_image_size = hparams.image_size;
|
||||
|
||||
@@ -2231,16 +2244,7 @@ struct clip_model_loader {
|
||||
{
|
||||
hparams.rope_theta = 10000.0f;
|
||||
get_u32(KEY_PROJ_SCALE_FACTOR, hparams.proj_scale_factor);
|
||||
|
||||
// borrowed from llava-1.6
|
||||
const int isize = hparams.image_size;
|
||||
hparams.image_grid_pinpoints = {
|
||||
isize, isize*2, // 336, 672
|
||||
isize*2, isize, // 672, 336
|
||||
isize*2, isize*2, // 672, 672
|
||||
isize*3, isize, // 1008, 336
|
||||
isize, isize*3, // 336, 1008
|
||||
};
|
||||
set_llava_uhd_res_candidates(model, 3);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_ULTRAVOX:
|
||||
case PROJECTOR_TYPE_QWEN2A:
|
||||
@@ -2674,6 +2678,21 @@ struct clip_model_loader {
|
||||
output[i] = values[i];
|
||||
}
|
||||
}
|
||||
|
||||
void set_llava_uhd_res_candidates(clip_model & model, const int max_patches_per_side) {
|
||||
auto & hparams = model.hparams;
|
||||
for (int x = 1; x <= max_patches_per_side; x++) {
|
||||
for (int y = 1; y <= max_patches_per_side; y++) {
|
||||
if (x == 1 && y == 1) {
|
||||
continue; // skip the first point
|
||||
}
|
||||
hparams.image_res_candidates.push_back(clip_image_size{
|
||||
x*hparams.image_size,
|
||||
y*hparams.image_size,
|
||||
});
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
struct clip_init_result clip_init(const char * fname, struct clip_context_params ctx_params) {
|
||||
@@ -3028,36 +3047,41 @@ struct llava_uhd {
|
||||
bool padding_refined = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6)
|
||||
};
|
||||
|
||||
static int get_max_slices(struct clip_ctx * ctx) {
|
||||
if (clip_is_minicpmv(ctx)) {
|
||||
return 9;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) {
|
||||
slice_instructions res;
|
||||
const int patch_size = clip_get_patch_size(ctx);
|
||||
const int slice_size = clip_get_image_size(ctx);
|
||||
const int max_slice_nums = get_max_slices(ctx);
|
||||
const int original_width = original_size.width;
|
||||
const int original_height = original_size.height;
|
||||
const float log_ratio = log((float)original_width / original_height);
|
||||
const float ratio = (float)original_width * original_height / (slice_size * slice_size);
|
||||
const int multiple = fmin(ceil(ratio), max_slice_nums);
|
||||
const bool has_slices = (multiple > 1);
|
||||
const bool has_pinpoints = !ctx->model.hparams.image_grid_pinpoints.empty();
|
||||
|
||||
const bool has_slices = original_size.width > slice_size || original_size.height > slice_size;
|
||||
const bool has_pinpoints = !ctx->model.hparams.image_res_candidates.empty();
|
||||
|
||||
if (!has_slices) {
|
||||
// skip slicing logic
|
||||
res.overview_size = clip_image_size{slice_size, slice_size};
|
||||
res.refined_size = clip_image_size{0, 0};
|
||||
res.grid_size = clip_image_size{0, 0};
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
if (has_pinpoints) {
|
||||
// has pinpoints, use them to calculate the grid size (e.g. llava-1.6)
|
||||
auto refine_size = llava_uhd::select_best_resolution(
|
||||
ctx->model.hparams.image_grid_pinpoints,
|
||||
original_size);
|
||||
original_size,
|
||||
ctx->model.hparams.image_res_candidates);
|
||||
res.overview_size = clip_image_size{slice_size, slice_size};
|
||||
res.refined_size = refine_size;
|
||||
res.grid_size = clip_image_size{0, 0};
|
||||
res.padding_refined = true;
|
||||
|
||||
LOG_DBG("%s: using pinpoints for slicing\n", __func__);
|
||||
LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n",
|
||||
__func__, original_width, original_height,
|
||||
res.overview_size.width, res.overview_size.height,
|
||||
res.refined_size.width, res.refined_size.height);
|
||||
|
||||
for (int y = 0; y < refine_size.height; y += slice_size) {
|
||||
for (int x = 0; x < refine_size.width; x += slice_size) {
|
||||
slice_coordinates slice;
|
||||
@@ -3066,13 +3090,16 @@ struct llava_uhd {
|
||||
slice.size.width = std::min(slice_size, refine_size.width - x);
|
||||
slice.size.height = std::min(slice_size, refine_size.height - y);
|
||||
res.slices.push_back(slice);
|
||||
if (x == 0) {
|
||||
res.grid_size.width++;
|
||||
}
|
||||
LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
|
||||
__func__, (int)res.slices.size() - 1,
|
||||
slice.x, slice.y, slice.size.width, slice.size.height);
|
||||
}
|
||||
res.grid_size.height++;
|
||||
}
|
||||
|
||||
res.grid_size.height = refine_size.height / slice_size;
|
||||
res.grid_size.width = refine_size.width / slice_size;
|
||||
LOG_DBG("%s: grid size: %d x %d\n", __func__, res.grid_size.width, res.grid_size.height);
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
@@ -3081,17 +3108,23 @@ struct llava_uhd {
|
||||
auto best_size = get_best_resize(original_size, slice_size, patch_size, !has_slices);
|
||||
res.overview_size = best_size;
|
||||
|
||||
if (!has_slices) {
|
||||
// skip slicing logic
|
||||
res.refined_size = clip_image_size{0, 0};
|
||||
res.grid_size = clip_image_size{0, 0};
|
||||
{
|
||||
const int max_slice_nums = 9; // TODO: this is only used by minicpmv, maybe remove it
|
||||
const float log_ratio = log((float)original_width / original_height);
|
||||
const float ratio = (float)original_width * original_height / (slice_size * slice_size);
|
||||
const int multiple = fmin(ceil(ratio), max_slice_nums);
|
||||
|
||||
} else {
|
||||
auto best_grid = get_best_grid(max_slice_nums, multiple, log_ratio);
|
||||
auto refine_size = get_refine_size(original_size, best_grid, slice_size, patch_size, true);
|
||||
res.grid_size = best_grid;
|
||||
res.refined_size = refine_size;
|
||||
|
||||
LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d, grid size: %d x %d\n",
|
||||
__func__, original_width, original_height,
|
||||
res.overview_size.width, res.overview_size.height,
|
||||
res.refined_size.width, res.refined_size.height,
|
||||
res.grid_size.width, res.grid_size.height);
|
||||
|
||||
int width = refine_size.width;
|
||||
int height = refine_size.height;
|
||||
int grid_x = int(width / best_grid.width);
|
||||
@@ -3108,7 +3141,9 @@ struct llava_uhd {
|
||||
slice.size.width = grid_x;
|
||||
slice.size.height = grid_y;
|
||||
res.slices.push_back(slice);
|
||||
// LOG_INF("slice %d: %d %d %d %d\n", ic, patches_i, patches_j, grid_x, grid_y);
|
||||
LOG_DBG("%s: slice %d: x=%d, y=%d, size=%dx%d\n",
|
||||
__func__, (int)res.slices.size() - 1,
|
||||
slice.x, slice.y, slice.size.width, slice.size.height);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -3166,48 +3201,55 @@ private:
|
||||
return res;
|
||||
}
|
||||
|
||||
static clip_image_size resize_maintain_aspect_ratio(const clip_image_size & orig, const clip_image_size & target_max) {
|
||||
float scale_width = static_cast<float>(target_max.width) / orig.width;
|
||||
float scale_height = static_cast<float>(target_max.height) / orig.height;
|
||||
float scale = std::min(scale_width, scale_height);
|
||||
return clip_image_size{
|
||||
static_cast<int>(orig.width * scale),
|
||||
static_cast<int>(orig.height * scale),
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* Selects the best resolution from a list of possible resolutions based on the original size.
|
||||
*
|
||||
* For example, when given a list of resolutions:
|
||||
* - 100x100
|
||||
* - 200x100
|
||||
* - 100x200
|
||||
* - 200x200
|
||||
*
|
||||
* And an input image of size 111x200, then 100x200 is the best fit (least wasted resolution).
|
||||
*
|
||||
* @param original_size The original size of the image
|
||||
* @param possible_resolutions A list of possible resolutions
|
||||
* @return The best fit resolution
|
||||
*/
|
||||
static clip_image_size select_best_resolution(const clip_image_size & original_size, const std::vector<clip_image_size> & possible_resolutions) {
|
||||
int original_width = original_size.width;
|
||||
int original_height = original_size.height;
|
||||
clip_image_size best_fit;
|
||||
int min_wasted_area = std::numeric_limits<int>::max();
|
||||
int max_effective_resolution = 0;
|
||||
int min_wasted_resolution = std::numeric_limits<int>::max();
|
||||
|
||||
for (const auto & resolution : possible_resolutions) {
|
||||
int width = resolution.width;
|
||||
int height = resolution.height;
|
||||
float scale = std::min(static_cast<float>(width) / original_width, static_cast<float>(height) / original_height);
|
||||
int downscaled_width = static_cast<int>(original_width * scale);
|
||||
int downscaled_height = static_cast<int>(original_height * scale);
|
||||
int effective_resolution = std::min(downscaled_width * downscaled_height, original_width * original_height);
|
||||
int wasted_resolution = (width * height) - effective_resolution;
|
||||
// LOG_INF("resolution: %d %d, scale: %f, downscaled: %d %d, effective: %d, wasted: %d\n", width, height, scale, downscaled_width, downscaled_height, effective_resolution, wasted_resolution);
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_resolution < min_wasted_resolution)) {
|
||||
for (const clip_image_size & candidate : possible_resolutions) {
|
||||
auto target_size = resize_maintain_aspect_ratio(original_size, candidate);
|
||||
int effective_resolution = std::min(
|
||||
target_size.width * target_size.height,
|
||||
original_size.width * original_size.height);
|
||||
int wasted_area = (candidate.width * candidate.height) - effective_resolution;
|
||||
|
||||
if (effective_resolution > max_effective_resolution || (effective_resolution == max_effective_resolution && wasted_area < min_wasted_area)) {
|
||||
max_effective_resolution = effective_resolution;
|
||||
min_wasted_resolution = wasted_resolution;
|
||||
best_fit = resolution;
|
||||
min_wasted_area = wasted_area;
|
||||
best_fit = candidate;
|
||||
}
|
||||
|
||||
LOG_DBG("%s: candidate: %d x %d, target: %d x %d, wasted: %d, effective: %d\n", __func__, candidate.width, candidate.height, target_size.width, target_size.height, wasted_area, effective_resolution);
|
||||
}
|
||||
|
||||
return best_fit;
|
||||
}
|
||||
|
||||
// used by llava 1.6 with custom list of pinpoints
|
||||
static clip_image_size select_best_resolution(const std::vector<int32_t> & pinpoints, const clip_image_size & original_size) {
|
||||
std::vector<clip_image_size> possible_resolutions; // TODO @ngxson : construct this inside hparams, not here
|
||||
for (size_t i = 0; i < pinpoints.size(); i += 2) {
|
||||
possible_resolutions.push_back(clip_image_size{pinpoints[i], pinpoints[i+1]});
|
||||
}
|
||||
return select_best_resolution(original_size, possible_resolutions);
|
||||
}
|
||||
|
||||
static int ensure_divide(int length, int patch_size) {
|
||||
return std::max(static_cast<int>(std::round(static_cast<float>(length) / patch_size) * patch_size), patch_size);
|
||||
}
|
||||
@@ -3331,7 +3373,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
||||
return true;
|
||||
|
||||
} else if (ctx->proj_type() == PROJECTOR_TYPE_LLAMA4) {
|
||||
GGML_ASSERT(!params.image_grid_pinpoints.empty());
|
||||
GGML_ASSERT(!params.image_res_candidates.empty());
|
||||
auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
|
||||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||||
|
||||
@@ -3371,7 +3413,7 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
||||
res_imgs->entries.push_back(std::move(res));
|
||||
return true;
|
||||
|
||||
} else if (!params.image_grid_pinpoints.empty()) {
|
||||
} else if (!params.image_res_candidates.empty()) {
|
||||
// "spatial_unpad" with "anyres" processing for llava-1.6
|
||||
auto const inst = llava_uhd::get_slice_instructions(ctx, original_size);
|
||||
std::vector<clip_image_u8_ptr> imgs = llava_uhd::slice_image(img, inst);
|
||||
@@ -3431,17 +3473,6 @@ const char * clip_patch_merge_type(const struct clip_ctx * ctx) {
|
||||
return ctx->model.hparams.mm_patch_merge_type == PATCH_MERGE_SPATIAL_UNPAD ? "spatial_unpad" : "flat";
|
||||
}
|
||||
|
||||
const int32_t * clip_image_grid(const struct clip_ctx * ctx) {
|
||||
if (ctx->model.hparams.image_grid_pinpoints.size()) {
|
||||
return &ctx->model.hparams.image_grid_pinpoints.front();
|
||||
}
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
size_t get_clip_image_grid_size(const struct clip_ctx * ctx) {
|
||||
return ctx->model.hparams.image_grid_pinpoints.size();
|
||||
}
|
||||
|
||||
int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 * img) {
|
||||
const auto & params = ctx->model.hparams;
|
||||
const int n_total = clip_n_output_tokens(ctx, img);
|
||||
|
||||
@@ -46,9 +46,6 @@ int32_t clip_get_hidden_size(const struct clip_ctx * ctx);
|
||||
// TODO: should be enum, not string
|
||||
const char * clip_patch_merge_type(const struct clip_ctx * ctx);
|
||||
|
||||
const int32_t * clip_image_grid(const struct clip_ctx * ctx);
|
||||
size_t get_clip_image_grid_size(const struct clip_ctx * ctx);
|
||||
|
||||
int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * img);
|
||||
|
||||
// for M-RoPE, this will be the number of token positions in X and Y directions
|
||||
|
||||
@@ -501,7 +501,10 @@ struct mtmd_tokenizer {
|
||||
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_MINICPMV_2_6
|
||||
|| ctx->slice_tmpl == MTMD_SLICE_TMPL_LLAMA4
|
||||
) {
|
||||
const int n_col = batch_f32.grid_x;
|
||||
const int n_row = batch_f32.grid_y;
|
||||
// split batch into chunks of single images
|
||||
// NOTE: batch_f32 will be invalidated after this call
|
||||
auto chunks = split_batch_to_chunk(std::move(batch_f32), bitmap->id);
|
||||
GGML_ASSERT(chunks.size() > 0);
|
||||
|
||||
@@ -521,8 +524,7 @@ struct mtmd_tokenizer {
|
||||
|
||||
// add slices (or tiles)
|
||||
if (!chunks.empty()) {
|
||||
const int n_col = batch_f32.grid_x;
|
||||
const int n_row = batch_f32.grid_y;
|
||||
GGML_ASSERT((int)chunks.size() == n_row * n_col);
|
||||
if (ctx->tok_slices_start != LLAMA_TOKEN_NULL) {
|
||||
add_text({ctx->tok_slices_start});
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user